Impact of water deficit on growth, productivity, and water use efficiency in potato genotypes (Solanum tuberosum L.)

1 Setup

setup <- tempfile(fileext = ".r")
readLines(con = 'https://inkaverse.com/setup.r') |> 
  gsub("echo = FALSE", "echo = TRUE", x = _) |>
  writeLines(setup)
source(setup)

library(gtsummary)
library(factoextra)
library(corrplot)

sessionInfo()

R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22622)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] corrplot_0.92       factoextra_1.0.7    gtsummary_1.6.1    
 [4] citr_0.3.2          huito_0.2.0         cowplot_1.1.1      
 [7] magick_2.7.3        psych_2.2.5         FactoMineR_2.4     
[10] googledrive_2.0.0   googlesheets4_1.0.0 gsheet_0.4.5       
[13] forcats_0.5.1       stringr_1.4.0       purrr_0.3.4        
[16] readr_2.1.2         tidyverse_1.3.2     knitr_1.39         
[19] inti_0.5.6          tibble_3.1.7        tidyr_1.2.0        
[22] dplyr_1.0.9         ggplot2_3.3.6       shiny_1.7.2        
[25] devtools_2.4.3      usethis_2.1.6      

loaded via a namespace (and not attached):
  [1] minqa_1.2.4          colorspace_2.0-3     ellipsis_0.3.2      
  [4] showtext_0.9-5       estimability_1.4     fs_1.5.2            
  [7] rstudioapi_0.13      showtextdb_3.0       remotes_2.4.2       
 [10] ggrepel_0.9.1        DT_0.23              fansi_1.0.3         
 [13] mvtnorm_1.1-3        lubridate_1.8.0      xml2_1.3.3          
 [16] splines_4.2.1        leaps_3.1            mnormt_2.1.0        
 [19] cachem_1.0.6         pkgload_1.3.0        jsonlite_1.8.0      
 [22] nloptr_2.0.3         gt_0.6.0             broom_1.0.0         
 [25] cluster_2.1.3        dbplyr_2.2.1         compiler_4.2.1      
 [28] httr_1.4.3           emmeans_1.7.5        backports_1.4.1     
 [31] assertthat_0.2.1     Matrix_1.4-1         fastmap_1.1.0       
 [34] gargle_1.2.0         cli_3.3.0            later_1.3.0         
 [37] htmltools_0.5.3      prettyunits_1.1.1    tools_4.2.1         
 [40] gtable_0.3.0         agricolae_1.3-5      glue_1.6.2          
 [43] rappdirs_0.3.3       Rcpp_1.0.9           cellranger_1.1.0    
 [46] vctrs_0.4.1          nlme_3.1-157         broom.helpers_1.8.0 
 [49] xfun_0.31            ps_1.7.1             lme4_1.1-30         
 [52] rvest_1.0.2          mime_0.12            miniUI_0.1.1.1      
 [55] lifecycle_1.0.1      MASS_7.3-57          scales_1.2.0        
 [58] hms_1.1.1            promises_1.2.0.1     parallel_4.2.1      
 [61] curl_4.3.2           yaml_2.3.5           memoise_2.0.1       
 [64] labelled_2.9.1       stringi_1.7.8        highr_0.9           
 [67] klaR_1.7-1           AlgDesign_1.2.1      boot_1.3-28         
 [70] pkgbuild_1.3.1       rlang_1.0.4          pkgconfig_2.0.3     
 [73] evaluate_0.15        lattice_0.20-45      htmlwidgets_1.5.4   
 [76] processx_3.7.0       tidyselect_1.1.2     magrittr_2.0.3      
 [79] R6_2.5.1             generics_0.1.3       combinat_0.0-8      
 [82] DBI_1.1.3            pillar_1.8.0         haven_2.5.0         
 [85] withr_2.5.0          scatterplot3d_0.3-41 modelr_0.1.8        
 [88] crayon_1.5.1         questionr_0.7.7      utf8_1.2.2          
 [91] tzdb_0.3.0           rmarkdown_2.14       sysfonts_0.8.8      
 [94] readxl_1.4.0         callr_3.7.1          reprex_2.0.1        
 [97] digest_0.6.29        flashClust_1.01-2    xtable_1.8-4        
[100] httpuv_1.6.5         openssl_2.0.2        munsell_0.5.0       
[103] askpass_1.1          sessioninfo_1.2.2   

2 Googlesheets connect

url <- "https://docs.google.com/spreadsheets/d/1dfgpmCKdPmxRHozrZp0iE_xMGsvKTIcztDpMWYSEGaY"
# browseURL(url)
gs <- as_sheets_id(url)

3 Trait rename

geno <- c("CIP720088" = "G01"
          , "CIP392797.22" = "G02"
          , "CIP397077.16" = "G03"
          , "CIP398192.213" = "G04"
          , "CIP398180.612" = "G05"
          , "CIP398208.704" = "G06"
          , "CIP398098.119" = "G07"
          , "CIP398190.89" = "G08"
          , "CIP398192.592" = "G09"
          , "CIP398201.510" = "G10"
          , "CIP398203.244" = "G11"
          , "CIP398203.5" = "G12"
          , "CIP398208.219" = "G13"
          , "CIP398208.33" = "G14"
          , "CIP398208.620" = "G15")

traits <- c("SPAD_29" = "Chlorophyll concentration (SPAD) at 29 dap"
            , "SPAD_59" = "Chlorophyll concentration (SPAD) at 59 dap"
            , "SPAD_76" = "Chlorophyll concentration (SPAD) at 76 dap"
            , "SPAD_83" = "Chlorophyll concentration (SPAD) at 83 dap"
            , "HGT" = "Plant height (cm)"
            , "RWC" = "Relative water content (%)"
            , "LOP" = "Leaf osmotic potential (MPa)"
            , "LDW" = "Leaf dry weight (g)"
            , "SDW" = "Stem dry weight (g)"
            , "RDW" = "Root dry weight (g)"
            , "TDW" = "Tuber dry weight (g)"
            , "NTUB" = "Tuber number (N°)" 
            , "TRS" = "Total transpiration (mL)"
            , "LFA" = "Leaf area (cm^2^)"
            , "RTL" = "Root length (cm)"
            , "TDB" = "Total dry biomass (g)"
            , "HI"  = "Harvest index (HI)" 
            , "SLA" = "Specific leaf area (cm^2^/g)"
            , "RCC" = "Relative chlorophyll content (RCC)"
            , "WUE_B" = "Biomass water use efficiency (g/L)"
            , "WUE_T" = "Tuber water use efficiency (g/L)"
            )

4 Import data

fb <- gs %>% 
  range_read("fb") %>% 
  rename_with("tolower") %>%
  rename_with(~gsub("\\s+|\\.", "_", .)) %>% 
  mutate(treat = case_when(treat %in% "wellwater" ~ "WW"
                           , treat %in% "drydown" ~ "WD")) %>% 
  select(block, treat, genotype,
         spad_29 = spad_29dap, 
         spad_59 = spad_59dap, 
         spad_76 = spad_76dap, 
         spad_83 = spad_83dap,
         hgt = hgt_86dap,
         rwc = rwc_84dap,
         lop = op_84dap,
         ldw = leafdw,
         sdw = stemdw,
         rdw = rootdw,
         tdw = tubdw,
         ntub,
         trs = ttrns,
         lfa = la,
         rtl = rlg
         ) %>% 
  mutate(tdb = (ldw+sdw+rdw+tdw),
         hi = tdw/(ldw+sdw+rdw+tdw),
         sla = lfa/ldw,
         rcc = (spad_83/lfa),
         wue_b = (ldw+sdw+rdw+tdw)/trs,
         wue_t = tdw/trs
         ) %>% 
  mutate(gnt = recode(genotype, !!!geno), .after = genotype) %>% 
  select(genotype, gnt, treat, block, everything()) %>% 
  mutate(across(where(is.character), as.factor)) 

# str(fb)

fb %>% web_table()

5 Abbreviations

gs %>% 
  range_read("var") %>% 
  filter(include == "x") %>%
  select(Variable, Abbreviation) %>%
  kable()

Abbreviations

Variable	Abbreviation
Soil Plant Analysis Development	SPAD
Plant height	HGT
Relative water content	RWC
Leaf osmotic potential	LOP
Leaf dry weight	LDW
Stem dry weight	SDW
Root dry weight	RDW
Tuber dry weight	TDW
Tuber number	NTUB
Root length	RTL
Total transpiration	TRS
Leaf area	LFA
Total dry biomass	TDB
Harvest index	HI
Specific leaf area	SLA
Water use efficiency	WUE
Tuber water use efficiency	TWUE

6 Table 1

tab <- gs %>% 
  range_read("gnt") %>% 
  dplyr::select(Number, Genotypes, Adaptability, "Growing period" = GPL, "Heat tolerance" = Heat, "Dry matter (%)")
  
# tab %>% sheet_write(gs, data = .,  sheet = "tab1")

tab %>% web_table()

7 Table 2

tab <- fb %>%
  select(!c(block, gnt, genotype)) %>% 
  tbl_summary(
    by = treat, 
    statistic = list(all_continuous() ~ "{mean} ± {sd}"),
    missing = "no") %>% 
  add_p() %>% 
  bold_labels() %>% 
  as_tibble() %>% 
  mutate(`**Characteristic**` = recode(
    `**Characteristic**`
    , "__spad_29__" = "Chlorophyll concentration (SPAD) at 29 dap"
    , "__spad_59__" = "Chlorophyll concentration (SPAD) at 59 dap"
    , "__spad_76__" = "Chlorophyll concentration (SPAD) at 76 dap"
    , "__spad_83__" = "Chlorophyll concentration (SPAD) at 83 dap"
    , "__hgt__" = "Plant height (cm)"
    , "__rwc__" = "Relative water content (%)"
    , "__lop__" = "Leaf osmotic potential (MPa)"
    , "__ldw__" = "Leaf dry weight (g)"
    , "__sdw__" = "Stem dry weight (g)"
    , "__rdw__" = "Root dry weight (g)"
    , "__tdw__" = "Tuber dry weight (g)"
    , "__ntub__" = "Tuber number (N°)" 
    , "__trs__" = "Total transpiration (mL)"
    , "__lfa__" = "Leaf area (cm^2^)"
    , "__rtl__" = "Root length (cm)"
    , "__tdb__" = "Total dry biomass (g)"
    , "__hi__"  = "Harvest index (HI)" 
    , "__sla__" = "Specific leaf area (cm^2^/g)"
    , "__rcc__" = "Relative chlorophyll content (RCC)"
    , "__wue_b__" = "Biomass water use efficiency (g/L)"
    , "__wue_t__" = "Tuber water use efficiency (g/L)"
    )) %>% 
  rename(Variable = `**Characteristic**`
         , `Water deficit` = `**WD**, N = 75`
         , `Well-Watered`= `**WW**, N = 75`
         , `p-value` = `**p-value**`) 

# tab %>% sheet_write(gs, data = .,  sheet = "tab2")

tab %>% web_table()

8 Figure 1

fts <- gs %>% 
  range_read("ftsw") %>%
  filter(Treatment != "preharvest") %>%
  tidyr::gather(key = day, value = fts, -ID, -Genotype, -Treatment)

model <- fts %>% 
  inti::mean_comparison(response = "fts"
                        , model_factors = "Treatment*day"
                        , c("Treatment", "day"))

plt1 <- model$comparison %>% 
  mutate(Treatment = case_when(
    Treatment == "drydown" ~ "Water Deficit (WD)"
    , Treatment == "wellwater" ~ "Well Watered (WW)"
  )) %>% 
  mutate(across(day, as.numeric)) %>% 
  plot_smr(
  type = "line", color = F
  , x = "day"
  , y = "fts"
  , group = "Treatment"
  , ylab = "Fraction of transpirable soil water"
  , xlab =  "Days"
  , glab = "Treatment"
  , legend = "none"
  , ylimits = c(0, 1.08, 0.1)
  , error =  "ste"
  ) +
  scale_x_continuous(breaks = c(0:100)*2
                     , limits = c(34, 88))

trns <- gs %>% 
  range_read("trans") %>%
  filter(Treatment != "preharvest") %>%
  tidyr::gather(key = day, value = trans, -ID, -Genotype, -Treatment) %>%
  filter(day != "TOTAL") %>%
  drop_na()


model <- trns %>% 
  inti::mean_comparison(response = "trans"
                        , model_factors = "Treatment*day"
                        , c("Treatment", "day"))

plt2 <- model$comparison %>% 
    mutate(Treatment = case_when(
    Treatment == "drydown" ~ "Water Deficit (WD)"
    , Treatment == "wellwater" ~ "Well Watered (WW)"
  )) %>% 
  mutate(across(day, as.numeric)) %>% 
  plot_smr(type = "line", color = F
            , x = "day"
            , y = "trans"
            , group = "Treatment"
            , ylab = "Transpiration (mL)"
            , xlab =  "Days"
            , glab = "Irrigation"
            , ylimits = c(0, 700, 100)
            , error = "ste"
            ) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

plot <- ggdraw(xlim = c(0, 0.5), ylim = c(0,0.9)) +
  draw_plot(plt2, width = 0.5, height = 0.43 , x = 0.0, y = 0.47 ) +
  draw_plot(plt1, width = 0.495, height = 0.5, x = 0.005, y = 0.0 ) +
          draw_plot_label(
            label = c("a", "b"),
            x = c(0.465, 0.465),
            y = c(0.79, 0.49))

ggsave2(plot = plot, "files/Fig1.tiff", width = 18, height = 15, units = "cm")

"files/Fig1.jpg" %>% include_graphics()

(A) Fraction of transpirable soil water (FTSW). (B) Daily transpiration in 15 potato genotypes under well-watered (WW) and water deficit (WD) condition.

9 Figure 2

# tdw

model <- fb %>% 
  inti::mean_comparison(response = "tdw"
                        , model_factors = "block + gnt*treat"
                        , c("gnt","treat"))

tdw <- model$comparison %>% 
  plot_smr(type = "bar"
          , color = F
          , x = "gnt"
          , xlab = ""
          , y = "tdw"
          , ylab = "Tuber dry weight (g)"
          , group = "treat"
          , glab = "Treatment"
          , legend = "none"
          , error = "ste"
          , ylimits = c(0, 100, 20)
          )

# rcc

model <- fb %>% 
  inti::mean_comparison(response = "rcc"
                        , model_factors = "block + gnt*treat"
                        , c("gnt","treat"))

rcc <- model$comparison %>% 
  plot_smr(type = "bar"
        , color = F
        , x = "gnt"
        , xlab =  ""
        , y = "rcc"
        , ylab = "Relative chlorophyll content"
        , ylimits = c(0, 0.1, 0.02)
        , group = "treat"
        , glab = "Treatment"
        , legend = "none"
        , error = "ste"
        ) 

# hi

model <- fb %>% 
  inti::mean_comparison(response = "hi"
                        , model_factors = "block + gnt*treat"
                        , c("gnt","treat"))

hi <- model$comparison %>% 
  plot_smr(type = "bar"
        , color = F
        , x = "gnt"
        , xlab =  ""
        , y = "hi"
        , ylab = "Harvest Index"
        , group = "treat"
        , glab = "Treatment"
        , legend = "none"
        , ylimits = c(0, 1, 0.2)
        , error = "ste"
        ) 

# wue_t

model <- fb %>% 
  inti::mean_comparison(response = "wue_t"
                        , model_factors = "block + gnt*treat"
                        , c("gnt","treat"))

wue_t <- model$comparison %>% 
  plot_smr(type = "bar", color = F
        , x = "gnt"
        , xlab =  ""
        , y = "wue_t"
        , ylab = "Tuber water use efficiency (g/L)"
        , group = "treat"
        , glab = "Treatment"
        , legend = "none"
        , ylimits = c(0, 10, 2)
        , error = "ste"
        ) 

# spad_29

model <- fb %>% 
  inti::mean_comparison(response = "spad_29"
                        , model_factors = "block + gnt*treat"
                        , c("gnt","treat"))

spad_29 <- model$comparison %>% 
    mutate(treat  = case_when(
    treat  == "WD" ~ "Water Deficit (WD)"
    , treat  == "WW" ~ "Well Watered (WW)"
    )) %>%
  plot_smr(type = "line"
        , x = "gnt"
        , xlab =  ""
        , y = "spad_29"
        , ylab = "SPAD at 29 dap"
        , group = "treat"
        , glab = "Treatment"
        , legend = "top"
        , ylimits = c(30, 70, 5)
        , error = "ste"
        , color = F
        )

# spad_83

model <- fb %>% 
  inti::mean_comparison(response = "spad_83"
                        , model_factors = "block + gnt*treat"
                        , c("gnt","treat"))

spad_83 <- model$comparison %>% 
  plot_smr(type = "line", color = F
        , x = "gnt"
        , xlab =  ""
        , y = "spad_83"
        , ylab = "SPAD at 83 dap"
        , group = "treat"
        , glab = "Treatment"
        , legend= "none"
        , ylimits = c(30, 70, 5)
        , error = "ste"
        ) 

plot <- ggdraw(xlim = c(0.0, 1), ylim = c(0.0, 1)) +
  draw_plot(spad_29 , width = 0.49, height = 0.35, x = 0.0, y = 0.6) +
  draw_plot(spad_83, width = 0.49, height = 0.3, x = 0.5, y = 0.6) +
  draw_plot(rcc,  width = 0.49, height = 0.3, x = 0.0, y = 0.3) +
  draw_plot(tdw, width = 0.49, height = 0.3, x = 0.5, y = 0.3) +
  draw_plot(hi , width = 0.49, height = 0.3, x = 0.0, y = 0.0) +
  draw_plot(wue_t, width = 0.49, height = 0.3, x = 0.5, y = 0.0) +
          draw_plot_label(
            label = c("a", "b", "c", "d", "e", "f"),
            x = c(0.06, 0.56, 0.06, 0.56, 0.06, 0.56),
            y = c(0.89, 0.89, 0.59, 0.59, 0.29, 0.29))

ggsave(plot = plot, "files/Fig2.tiff", units = "cm", width = 28, height = 25)

"files/Fig2.jpg" %>% include_graphics()

Traits measured in 15 potato genotypes under well-watered (WW) and water deficit (WD) condition. (A-B) Chlorophyll concentration. (C) Tuber dry weight. (D) Relative chlorophyll content. (E) Harvest Index. (F) Tuber Water Use Efficiency. Error bars indicate standard error (n = 5). Days after planting (dap).

10 Figure 3

cr <- fb %>% 
  group_by(gnt, treat) %>% 
  summarise_all(list(mean), na.rm = TRUE) %>% 
  as.data.frame() %>% 
  select(!c( genotype, block)) %>% 
  unite(gnt, treat, col = "rn") %>% 
  column_to_rownames("rn") %>% 
  rename_with(toupper, where(is.numeric)) 

cp <- heatmaply::heatmaply_cor(
  cor(cr),
  k_col = 5, 
  dendrogram = c("column"),  
  grid_gap = 1,
  cellnote = round(cor(cr),2),
  cellnote_textposition = "middle center",
  cellnote_size = 10,
  file = "files/correlation.pdf"
  )

fig <- magick::image_read_pdf("files/correlation.pdf") %>% 
  grid::rasterGrob() %>%
  ggsave("files/Fig3.tiff", plot =  ., width = 8.39, height = 5.26)
  
unlink("files/correlation.pdf")

"files/Fig3.jpg" %>% include_graphics()

Relationships among agro-morphological traits evaluated in well-watered (WW) and water deficit (WD) treatments based on Pearson correlation and Euclidean distance measured in 15 potato genotypes.

Where: Chlorophyll Concentration (SPAD), Plant height (HGT; cm), Relative water content (RWC; %), Leaf osmotic potential (LOP; MPa), Leaf dry weight (LDW; g), Stem dry weight (SDW; g), Root dry weight (RDW; g), Tuber dry weight (TDW; g), Tuber number (NTUB; N°), Total transpiration (TRS; mL), Leaf area (LFA; cm²), Root length (RTL; cm), Total dry biomass (TDB; g), Harvest index (HI), Specific leaf area (SLA; cm²g^-1), Relative chlorophyll content (RCC), Biomass water use efficiency (WUE_B; gL^-1), Tuber water use efficiency (WUE_T; gL^-1).

11 Figure 4

mvd <- fb %>%
  select(!c(block, gnt)) %>%
  group_by(treat, genotype) %>%
  summarise(across(everything(), ~mean(.x, na.rm = T))) %>% 
  mutate(coln = paste(genotype, treat,  sep = "_")) %>%
  column_to_rownames("coln") %>%
  select(!genotype) %>% 
  rename_with(toupper, where(is.numeric))

pca <- PCA(mvd, graph = F, scale.unit = TRUE, quali.sup = 1)

# PCA

tmp <- tempfile(fileext = ".png")
ppi <- 300
png(tmp, width=8*ppi, height=8*ppi, res=ppi)
plot.PCA(pca,
         choix="var",
         title="",
         autoLab = "auto", 
         shadowtext = T)
graphics.off()

pt1 <- png::readPNG(tmp) %>%
  grid::rasterGrob()

# sink("files/pca.txt")
# # Results
# summary(pca, nbelements = Inf, nb.dec = 2)
# # Correlation de dimensions
# dimdesc(pca)
# sink()

# Analysis de Hierarchical Clustering

clus <- HCPC(pca, nb.clust=-1, graph = F)

tmp <- tempfile(fileext = ".png")
ppi <- 300
png(tmp, width=8*ppi, height=8*ppi, res=ppi)
plot.HCPC(clus,
          choice = "map",
          legend = list(bty = "y", x = "topright"), 
          draw.tree = F)
graphics.off()

pt2 <- png::readPNG(tmp) %>%
  grid::rasterGrob()

# sink("files/clu.txt")
# clus$call$t$tree
# clus$desc.ind
# clus$desc.var
# sink()

plot <- plot_grid(pt1, pt2, nrow = 1, labels = "auto")

ggsave2(plot = plot, "files/Fig4.tiff", units = "cm", width = 20, height = 11)

"files/Fig4.jpg" %>% include_graphics()

Principal Component Analysis (PCA) from variables measured in 15 potato genotypes under well-watered (WW) and water deficit (WD) condition. (A) PCA for the variables. (B) PCA for the genotypes under WW and WD.

Where: Chlorophyll Concentration (SPAD), Plant height (HGT; cm), Relative water content (RWC; %), Leaf osmotic potential (LOP; MPa), Leaf dry weight (LDW; g), Stem dry weight (SDW; g), Root dry weight (RDW; g), Tuber dry weight (TDW; g), Tuber number (NTUB; N°), Total transpiration (TRS; mL), Leaf area (LFA; cm²), Root length (RTL; cm), Total dry biomass (TDB; g), Harvest index (HI), Specific leaf area (SLA; cm²g^-1), Relative chlorophyll content (RCC), Biomass water use efficiency (WUE_B; gL^-1), Tuber water use efficiency (WUE_T; gL^-1).

12 Supplementary Table 1

vars <- names(fb)

model <- 5:length(vars) %>% map(function(x) {
  
    fb %>% 
      H2cal(trait = vars[x]
            , gen.name = "genotype"
            , rep.n = 4
            , fixed.model = "0 + (1|block) + genotype"
            , random.model = "1 + (1|block) + (1|genotype)"
            , emmeans = T
            , plot_diag = T
            , outliers.rm = T
            )
})

tabsmr <- 1:length(model) %>% map(function(x) {
      model[[x]][["tabsmr"]] 
      }) %>% 
  bind_rows() %>% 
  mutate(trait = recode(trait, !!!traits)) %>% 
  select(trait, mean, std, min, max, V.g, V.e, repeatability) %>% 
  mutate(across(where(is.numeric), ~ round(.x, digits = 2))) %>% 
  mutate(trait = toupper(trait)) %>% 
  mutate(trait = recode(trait, !!!traits)) %>% 
  rename(Trait = trait)
  
tabsmr  %>% web_table()

# tabsmr %>% sheet_write(gs, data = .,  sheet = "tabS1")

13 Supplementary Figure 1

var <- get_pca_var(pca)

tmp <- tempfile(fileext = ".png")
ppi <- 300
png(tmp, width=3.8*ppi, height=10*ppi, res=ppi)
corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,)
graphics.off()

pt1 <- png::readPNG(tmp) %>%
  grid::rasterGrob(interpolate = TRUE)

pt2 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 50) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 18) + 
  labs(title = "Dim 1 - variables contribution") 

pt4 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 18) + 
  labs(title = "Dim 2 - variables contribution") 

plot <- ggdraw(xlim = c(0.0, 1.0), ylim = c(0, 1.0))+
  draw_plot(pt1,  width = 0.4, height = 0.99, x = 0.6, y = 0.0) +  
  draw_plot(pt2,  width = 0.6, height = 0.34, x = 0.03, y = 0.66) +
  draw_plot(pt3, width = 0.6, height = 0.34, x = 0.03, y = 0.33) + 
  draw_plot(pt4, width = 0.6, height = 0.34, x = 0.03, y = 0.0) +
        draw_plot_label(
    label = c("a", "b", "c", "d"),
    x = c(0.005, 0.005, 0.005, 0.65),
    y = c(0.999, 0.67, 0.34, 0.999))

ggsave2(plot = plot, "files/SFig1.tiff", height = 25, width = 30, units = "cm")

"files/SFig1.jpg" %>% include_graphics()

Principal Component Analysis (PCA). (A) Percentage of the explained variance for each dimension. (B) Variance contribution of the first 10 variables in the dimension 1. (C) Variance contribution of the first 10 variables in the dimension 2. (D) Correlation between the studied variables and among the first 5 dimensions. The reference dashed lines on the bar plot corresponds to the expected value if the contribution between the variables where uniform.

14 Supplementary Figure 2

fig <- magick::image_read("files/potatos.png") %>% 
  grid::rasterGrob() %>%
  ggsave("files/SFig2.tiff", plot =  ., width = 13, height = 13)

"files/SFig2.jpg" %>% include_graphics()

Tuber yield performance from two contrasting potatoes genotypes. Genotype CIP 398203.244 with good performance under well-watered (WW) with reduced yields during water deficit (WD) condition. Genotype CIP 398190.89 with good response under well-watered (WW) and water deficit (WD) treatment. Each group represent one replication (n = 5). Pictures were taken using the 5 cm scale (black/white segment = 1 cm) displayed alongside the tubers.

15 Convert figures to jpg format

figs <- "files/"  %>%  list.files(pattern = "tiff", full.names = T)

convert <- 1:length(figs) %>% map(\(x) {
  
  figname <- figs[x] %>% basename() %>% gsub("tiff", "jpg", .)
  
  figs[x] %>% 
    tiff::readTIFF(., native=TRUE) %>% 
    jpeg::writeJPEG(., target = file.path("files", figname))
  
})